The signed regressor least mean fourth (SRLMF) adaptive algorithm

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" The signed regressor least mean fourth (SRLMF) adaptive algorithm "
Mohammed Mujahid Ulla Faiz A. Zerguine

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	55309
Doc. No	:	TL25263
Call number	:	‭1533994‬
Main Entry	:	Mohammed Mujahid Ulla Faiz
Title & Author	:	The signed regressor least mean fourth (SRLMF) adaptive algorithm\ Mohammed Mujahid Ulla Faiz
College	:	King Fahd University of Petroleum and Minerals (Saudi Arabia)
Date	:	2009
Degree	:	M.S.
student score	:	2009
Page No	:	133
Abstract	:	In this thesis, a novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment. An optimum value of the step-size μ is also derived. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex.
Subject	:	Applied sciences; Electrical engineering; 0544:Electrical engineering
Added Entry	:	A. Zerguine
Added Entry	:	King Fahd University of Petroleum and Minerals (Saudi Arabia)